First, we load, filter, and merge the data sets.
How does the data set looks like
Applied thresholds are indicated by grey horizontal line.
#Apply tresholds
data <- subset(data, Mean_Puncta_mitoTracker_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mitoTracker_Number_Object_Number < 1400)
data <- subset(data, mitoTracker_MeanArea < 0.04)
data <- subset(data, mitoTracker_MeanCount < 0.35)
data <- subset(data, mitoTracker_MeanLength < 0.07)
data <- subset(data, Branchpoints < 100)
#Save data set
write.csv(data, file = "results_mitoTracker/tables/data_mitoTracker.csv")
Cell counts per cell line:
#data <- read.csv("results_mitoTracker/tables/data_mitoTracker.csv")
table(data$Metadata_SampleID)
##
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015
## 626 108 482 0 393 529
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002
## 55 292 155 314 449 519
Mean cell count:
mean(table(data$Metadata_SampleID))
## [1] 326.8333
Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.
Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_AreaShape_Area ~ Disease_state + (1 |
## Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 37655.7 37680.8 -18823.8 37647.7 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3135 -0.7153 -0.2849 0.3660 5.2543
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 13.77 3.71
## Residual 859.40 29.32
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 41.1424 2.0452 20.116
## Disease_statesPD -0.2975 2.5854 -0.115
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.791
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_AreaShape_Area
## Chisq Df Pr(>Chisq)
## Disease_state 0.0132 1 0.9084
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Number_Object_Number ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 54356.5 54381.6 -27174.2 54348.5 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9826 -0.7879 -0.1063 0.6683 3.8460
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 3999 63.24
## Residual 60535 246.04
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 396.76 32.50 12.208
## Disease_statesPD 41.62 40.87 1.018
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.795
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Number_Object_Number
## Chisq Df Pr(>Chisq)
## Disease_state 1.0371 1 0.3085
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -9970.9 -9945.8 4989.4 -9978.9 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9958 -0.5368 -0.0929 0.4477 6.5618
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.003624 0.06020
## Residual 0.004528 0.06729
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.16013 0.03017 5.307
## Disease_statesPD 0.02586 0.03783 0.683
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.797
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker
## Chisq Df Pr(>Chisq)
## Disease_state 0.467 1 0.4944
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -26721.3 -26696.2 13364.7 -26729.3 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2166 -0.6724 -0.3272 0.3302 4.0370
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 2.921e-06 0.001709
## Residual 6.374e-05 0.007984
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0091144 0.0008883 10.261
## Disease_statesPD -0.0013920 0.0011182 -1.245
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.794
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanArea
## Chisq Df Pr(>Chisq)
## Disease_state 1.5496 1 0.2132
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -9545.0 -9519.9 4776.5 -9553.0 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5974 -0.7433 -0.2424 0.4793 3.6918
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.0005318 0.02306
## Residual 0.0050756 0.07124
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.085316 0.011736 7.269
## Disease_statesPD -0.004699 0.014744 -0.319
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.796
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanCount
## Chisq Df Pr(>Chisq)
## Disease_state 0.1016 1 0.7499
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 25258.8 25283.9 -12625.4 25250.8 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2547 -0.6947 -0.3338 0.3346 5.7230
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 6.777 2.603
## Residual 36.201 6.017
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.880 1.315 3.711
## Disease_statesPD 1.411 1.650 0.855
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.797
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.7309 1 0.3926
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 34891.7 34916.8 -17441.8 34883.7 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8271 -0.6447 -0.4118 0.3099 4.1707
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 47.16 6.867
## Residual 422.68 20.559
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 15.286 3.491 4.378
## Disease_statesPD 3.866 4.386 0.882
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.796
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Branchpoints
## Chisq Df Pr(>Chisq)
## Disease_state 0.7772 1 0.378
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 45382.3 45407.4 -22687.1 45374.3 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6787 -0.5941 -0.3749 0.2426 5.2837
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 2025 45.0
## Residual 6115 78.2
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 52.20 22.63 2.307
## Disease_statesPD 25.71 28.39 0.906
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.797
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.8202 1 0.3651
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -22949.9 -22924.8 11479.0 -22957.9 3918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2652 -0.6325 -0.4190 0.2908 4.7185
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 7.820e-06 0.002796
## Residual 1.667e-04 0.012913
## Number of obs: 3922, groups: Disease_state:Metadata_SampleID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.008381 0.001452 5.772
## Disease_statesPD 0.001726 0.001828 0.944
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.794
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanLength
## Chisq Df Pr(>Chisq)
## Disease_state 0.8919 1 0.345